def test_predictOn_model(self): """Test that the model predicts correctly on toy data.""" stkm = StreamingKMeans() stkm._model = StreamingKMeansModel(clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]], clusterWeights=[1.0, 1.0, 1.0, 1.0]) predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]] predict_data = [ self.sc.parallelize(batch, 1) for batch in predict_data ] predict_stream = self.ssc.queueStream(predict_data) predict_val = stkm.predictOn(predict_stream) result = [] def update(rdd): rdd_collect = rdd.collect() if rdd_collect: result.append(rdd_collect) predict_val.foreachRDD(update) self.ssc.start() def condition(): self.assertEqual(result, [[0], [1], [2], [3]]) return True eventually(condition, catch_assertions=True)
def test_predictOn_model(self): """Test that the model predicts correctly on toy data.""" stkm = StreamingKMeans() stkm._model = StreamingKMeansModel(clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]], clusterWeights=[1.0, 1.0, 1.0, 1.0]) predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]] predict_data = [sc.parallelize(batch, 1) for batch in predict_data] predict_stream = self.ssc.queueStream(predict_data) predict_val = stkm.predictOn(predict_stream) result = [] def update(rdd): rdd_collect = rdd.collect() if rdd_collect: result.append(rdd_collect) predict_val.foreachRDD(update) t = time() self.ssc.start() self._ssc_wait(t, 6.0, 0.01) self.assertEquals(result, [[0], [1], [2], [3]])
def test_predictOn_model(self): """Test that the model predicts correctly on toy data.""" stkm = StreamingKMeans() stkm._model = StreamingKMeansModel( clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]], clusterWeights=[1.0, 1.0, 1.0, 1.0]) predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]] predict_data = [self.sc.parallelize(batch, 1) for batch in predict_data] predict_stream = self.ssc.queueStream(predict_data) predict_val = stkm.predictOn(predict_stream) result = [] def update(rdd): rdd_collect = rdd.collect() if rdd_collect: result.append(rdd_collect) predict_val.foreachRDD(update) self.ssc.start() def condition(): self.assertEqual(result, [[0], [1], [2], [3]]) return True self._eventually(condition, catch_assertions=True)
def test_predictOn_model(self): """Test that the model predicts correctly on toy data.""" stkm = StreamingKMeans() stkm._model = StreamingKMeansModel( clusterCenters=[[1.0, 1.0], [-1.0, 1.0], [-1.0, -1.0], [1.0, -1.0]], clusterWeights=[1.0, 1.0, 1.0, 1.0]) predict_data = [[[1.5, 1.5]], [[-1.5, 1.5]], [[-1.5, -1.5]], [[1.5, -1.5]]] predict_data = [sc.parallelize(batch, 1) for batch in predict_data] predict_stream = self.ssc.queueStream(predict_data) predict_val = stkm.predictOn(predict_stream) result = [] def update(rdd): rdd_collect = rdd.collect() if rdd_collect: result.append(rdd_collect) predict_val.foreachRDD(update) t = time() self.ssc.start() self._ssc_wait(t, 6.0, 0.01) self.assertEquals(result, [[0], [1], [2], [3]])